针对分布式光纤在周界安防系统中信号种类, 即不同的环境下产生的噪声信号干扰和常见的入侵产生的信号。本文基于全光纤马赫—泽德干涉仪的分布式光纤传感模型, 提出了一种识别常见的越境信号和消除环境噪声干扰信号的方法, 实现了在去除环境干扰的情况下, 用 BP神经网络对多种入侵信号识别。实验结果证明, 该方法能够有效的区分越境信号和不同环境状态产生的噪声信号, 极大的提高了整个系统的识别率, 降低了其虚警率。

The signal source that may be found in perimeter security system of distributed Fiber-optic includes noise signals caused by different environmental state and intrusion signals caused by common kinds of invasion. In order to distinguish noise signals from intrusion signals, a new method based on the M-Z interferometers is proposed. In addition, by using BP neural network, we can recognize different targets while a variety of invasive signals through the Fiber-optic without any ambient noises. Experiment results show that the proposed method is able to differentiate intrusion signals from ambient noises effectively. What’s more, the recognition rate of the system is improved while the false alarm rate is reduced.